Abstract: This paper identifies leading triggers of deficient episodes in terms of GDP growth based on a sample of countries at different stages of development over 1994-2017. Using logit models, we build early warning systems (EWS) and our results show important differences between developing countries (DCs) and advanced economies (AEs). For AEs, the main predictors of the probability of entering in a GDP growth deficient episode are the deterioration of external imbalances and the vulnerability of fiscal position while DCs face different challenges that need to be considered. The key indicators for them are first, the low ability to pay its debts and second, their belonging or not to a common currency area. We also build homogeneous pools of countries inside AEs and DCs. For AEs, the evolution of the proportion of countries in the riskiest pool is marked first, by three distinct peaks just after the high-tech bubble burst, the global financial crisis and the European sovereign debt crisis, and second by a very low minimum level in 2006 and 2007. In contrast, the situation of DCs is characterized first by a relative stability of this proportion and then by an upward trend from 2006, that can be explained by more unfavorable socio-political environment leading to shortcomings in the fiscal consolidation.
Abstract: A reliable, real-time, and non-invasive system that can identify patients at risk for hemodynamic instability is needed to aid clinicians in their efforts to anticipate patient deterioration and initiate early interventions. The purpose of this pilot study was to explore the clinical capabilities of a real-time analytic from a single lead of an electrocardiograph to correctly distinguish between rapid response team (RRT) activations due to hemodynamic (H-RRT) and non-hemodynamic (NH-RRT) causes, as well as predict H-RRT cases with actionable lead times. The study consisted of a single center, retrospective cohort of 21 patients with RRT activations from step-down and telemetry units. Through electronic health record review and blinded to the analytic’s output, each patient was categorized by clinicians into H-RRT and NH-RRT cases. The analytic output and the categorization were compared. The prediction lead time prior to the RRT call was calculated. The analytic correctly distinguished between H-RRT and NH-RRT cases with 100% accuracy, demonstrating 100% positive and negative predictive values, and 100% sensitivity and specificity. In H-RRT cases, the analytic detected hemodynamic deterioration with a median lead time of 9.5 hours prior to the RRT call (range 14 minutes to 52 hours). The study demonstrates that an electrocardiogram (ECG) based analytic has the potential for providing clinical decision and monitoring support for caregivers to identify at risk patients within a clinically relevant timeframe allowing for increased vigilance and early interventional support to reduce the chances of continued patient deterioration.
Abstract: Regional earthquake early warning (EEW) systems are not suitable for Taiwan, as most destructive seismic hazards arise due to in-land earthquakes. These likely cause the lead-time provided by regional EEW systems before a destructive earthquake wave arrives to become null. On the other hand, an on-site EEW system can provide more lead-time at a region closer to an epicenter, since only seismic information of the target site is required. Instead of leveraging the information of several stations, the on-site system extracts some P-wave features from the first few seconds of vertical ground acceleration of a single station and performs a prediction of the oncoming earthquake intensity at the same station according to these features. Since seismometers could be triggered by non-earthquake events such as a passing of a truck or other human activities, to reduce the likelihood of false alarms, a seismometer was installed at three different locations on the same site and the performance of the EEW system for these three sensor locations were discussed. The results show that the location on the ground of the first floor of a school building maybe a good choice, since the false alarms could be reduced and the cost for installation and maintenance is the lowest.
Abstract: Traditional early warning systems that alarm against crisis are generally based on structured or numerical data; therefore, a system that can make predictions based on unstructured textual data, an uncorrelated data source, is a great complement to the traditional early warning systems. The Chicago Board Options Exchange (CBOE) Volatility Index (VIX), commonly referred to as the fear index, measures the cost of insurance against market crash, and spikes in the event of crisis. In this study, news data is consumed for prediction of whether there will be a market-wide crisis by predicting the movement of the fear index, and the historical references to similar events are presented in an unsupervised manner. Topic modeling-based prediction and representation are made based on daily news data between 1990 and 2015 from The Wall Street Journal against VIX index data from CBOE.
Abstract: This paper describes the issues relating to the role of
the flash flood early warning system provided by the Malaysian
Government to the communities in Malaysia, specifically during the
flash flood disaster in the Cameron Highlands, Malaysia. Normally,
flash flood disasters can occur as a result of heavy rainfall in an area,
and that water may possibly cause flooding via streams or narrow
channels. The focus of this study is the flash flood disaster which
occurred on 23 October 2013 in the Cameron Highlands, and as a
result the Sungai Bertam overflowed after the release of water from
the Sultan Abu Bakar Dam. This release of water from the dam
caused flash flooding which led to damage to properties and also the
death of residents and livestock in the area. Therefore, the effort of
this study is to identify the perceptions of the flash flood victims on
the role of the flash flood early warning system. For the purposes of
this study, data were gathered through face-to-face interviews from
those flood victims who were willing to participate in this study. This
approach helped the researcher to glean in-depth information about
their feelings and perceptions of the role of the flash flood early
warning system offered by the government. The data were analysed
descriptively and the findings show that the respondents of 22 flood
victims believe strongly that the flash flood early warning system was
confusing and dysfunctional, and communities had failed to response
positively to it. Therefore, most of the communities were not well
prepared for the releasing of water from the dam which caused
property damage, and 3 people were killed in the Cameron Highland
flash flood disaster.
Abstract: Flash Floods, together with landslides, are a common
natural threat for people living in mountainous regions and foothills.
One way to deal with this constant menace is the use of Early
Warning Systems, which have become a very important mitigation
strategy for natural disasters.
In this work we present our proposal for a pilot Flash Flood Early
Warning System for Santiago, Chile, the first stage of a more
ambitious project that in a future stage shall also include early
warning of landslides.
To give a context for our approach, we first analyze three existing
Flash Flood Early Warning Systems, focusing on their general
architectures. We then present our proposed system, with main focus
on the decision support system, a system that integrates empirical
models and fuzzy expert systems to achieve reliable risk estimations.
Abstract: One of the biggest problems of SMEs is their tendencies to financial distress because of insufficient finance background. In this study, an Early Warning System (EWS) model based on data mining for financial risk detection is presented. CHAID algorithm has been used for development of the EWS. Developed EWS can be served like a tailor made financial advisor in decision making process of the firms with its automated nature to the ones who have inadequate financial background. Besides, an application of the model implemented which covered 7,853 SMEs based on Turkish Central Bank (TCB) 2007 data. By using EWS model, 31 risk profiles, 15 risk indicators, 2 early warning signals, and 4 financial road maps has been determined for financial risk mitigation.
Abstract: More and more natural disasters are happening every
year: floods, earthquakes, volcanic eruptions, etc. In order to reduce
the risk of possible damages, governments all around the world are
investing into development of Early Warning Systems (EWS) for
environmental applications. The most important task of the EWS is
identification of the onset of critical situations affecting environment
and population, early enough to inform the authorities and general
public. This paper describes an approach for monitoring of flood
protections systems based on machine learning methods. An
Artificial Intelligence (AI) component has been developed for
detection of abnormal dike behaviour. The AI module has been
integrated into an EWS platform of the UrbanFlood project (EU
Seventh Framework Programme) and validated on real-time
measurements from the sensors installed in a dike.
Abstract: During last decades, worldwide researchers dedicated
efforts to develop machine-based seismic Early Warning systems,
aiming at reducing the huge human losses and economic damages.
The elaboration time of seismic waveforms is to be reduced in order
to increase the time interval available for the activation of safety
measures. This paper suggests a Data Mining model able to correctly
and quickly estimate dangerousness of the running seismic event.
Several thousand seismic recordings of Japanese and Italian
earthquakes were analyzed and a model was obtained by means of a
Bayesian Network (BN), which was tested just over the first
recordings of seismic events in order to reduce the decision time and
the test results were very satisfactory.
The model was integrated within an Early Warning System
prototype able to collect and elaborate data from a seismic sensor
network, estimate the dangerousness of the running earthquake and
take the decision of activating the warning promptly.
Abstract: During the last couple of years, the degree of dependence on IT systems has reached a dimension nobody imagined to be possible 10 years ago. The increased usage of mobile devices (e.g., smart phones), wireless sensor networks and embedded devices (Internet of Things) are only some examples of the dependency of modern societies on cyber space. At the same time, the complexity of IT applications, e.g., because of the increasing use of cloud computing, is rising continuously. Along with this, the threats to IT security have increased both quantitatively and qualitatively, as recent examples like STUXNET or the supposed cyber attack on Illinois water system are proofing impressively. Once isolated control systems are nowadays often publicly available - a fact that has never been intended by the developers. Threats to IT systems don’t care about areas of responsibility. Especially with regard to Cyber Warfare, IT threats are no longer limited to company or industry boundaries, administrative jurisdictions or state boundaries. One of the important countermeasures is increased cooperation among the participants especially in the field of Cyber Defence. Besides political and legal challenges, there are technical ones as well. A better, at least partially automated exchange of information is essential to (i) enable sophisticated situational awareness and to (ii) counter the attacker in a coordinated way. Therefore, this publication performs an evaluation of state of the art Intrusion Detection Message Exchange protocols in order to guarantee a secure information exchange between different entities.